AI-ASSISTED NANOMEDICINE REVEALS SYSTEMIC IMMUNE IMPACT IN OVARIAN CANCER METASTASIS

Abstract

Ovarian cancer, a gynecological malignancy, poses a significant challenge as researchers struggle to comprehend how tumor cells communicate with the immune system. Ovarian tumors contain both bulk cancer cells and cancer stem cells (CSCs), which are linked to chemoresistance, relapse, and immune escape. However, it remains unclear whether these two populations affect immune cells differently. To investigate this, a study used surface-enhanced Raman spectroscopy (SERS) combined with machine learning to examine the biochemical changes in peripheral blood mononuclear cells (PBMCs) after co-culture with OVCAR4 ovarian cancer cells or OVCAR4-derived CSC-enriched populations. PBMCs and tumor cells were co-cultured for 48 hours in an indirect Transwell system, allowing exchange of soluble factors without direct cell contact. SERS spectra were acquired on gold-coated nickel substrates and analyzed using principal component analysis, t-test, and a linear support vector machine (SVM). The results showed that PBMCs co-cultured with CSCs and PBMCs co-cultured with OVCAR4 cells exhibited distinct spectral profiles. Moreover, the SVM demonstrated a high cross-validated accuracy of 98.9% in separating these two populations. PBMCs exposed to CSCs exhibited higher adenine and carbohydrate signals, while those exposed to OVCAR4 cells displayed higher protein and lipid signals. Interestingly, the tumor cells themselves exhibited the same pattern at baseline. Moreover, six out of the ten most discriminating bands were shared between the tumor and PBMC comparisons, indicating a consistent direction of change. These findings suggest that tumor-derived soluble factors leave a distinct biochemical signature on PBMCs. Label-free SERS combined with machine learning offers a sensitive approach for studying tumor-immune communication and developing blood-based tools to monitor ovarian cancer.